code: def __init__(
      self,
      embd_dim,
      video_modality_dim,
      text_dim,
      gating=True,
      text_cluster=32,
  ):
      super(Net2, self).__init__()
  
      self.text_pooling = NetVLAD(
          feature_size=text_dim, cluster_size=text_cluster
      )
      self.embd_text = Gated_Embedding_Unit(
          self.text_pooling.out_dim, embd_dim, gating=gating
      )
      self.embd_video = Gated_Embedding_Unit(
          video_modality_dim, embd_dim, gating=gating
      )
      self.audio_pooling = NetVLAD(feature_size=128, cluster_size=16)
  
imports: from torch.autograd import Variable
  import torch.nn
  import torch.nn.functional
  import torch
  from loupe import NetVLAD
  import numpy
  from torch.autograd import Function
label_desc: Use image node_id 1 to create a new Net2 object from inherited base classes: nn with example: obj = Net2(embd_dim, video_modality_dim, text_dim, gating, text_cluster)